{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# White Christmas\n",
    "\n",
    "## HadCET\n",
    "The [HadCET](http://www.metoffice.gov.uk/hadobs/hadcet/) data is \"the longest available instrumental record of temperature in the world\", currently available from the UK Met Office. It provides the daily mean temperature for the centre of England since 1772.\n",
    "\n",
    "From that table you can see that the mean daily temperature for 1st January 2001 was recorded as 40 meaning 4.0°C, the temperature for 2nd January 2001 has 77 which translates to 7.7°C\n",
    "\n",
    "The February temperatures are in the m2 column, notice that -999 has been entered for invalid cells such as 31 Feb 2001.\n",
    "\n",
    "```\n",
    "+------+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n",
    "| yr   | dy |  m1 |  m2 |  m3 |  m4 |  m5 |  m6 |  m7 |  m8 |  m9 | m10 | m11 | m12 |\n",
    "+------+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n",
    "| 2001 |  1 |  40 |  11 |   9 | 112 |  87 | 131 | 185 | 180 | 150 | 151 |  91 |  98 |\n",
    "| 2001 |  2 |  77 |  32 |  -4 | 114 |  99 | 126 | 207 | 163 | 168 | 143 |  99 |  29 |\n",
    "| 2001 |  3 |  52 |  66 |  -3 |  84 | 105 | 100 | 209 | 166 | 153 | 142 |  87 |  55 |\n",
    "| 2001 |  4 |  50 |  57 |   5 |  69 |  72 | 130 | 219 | 151 | 134 | 139 |  90 |  73 |\n",
    "| 2001 |  5 |  47 |  68 |  12 |  71 |  82 | 136 | 217 | 161 | 139 | 159 |  75 |  82 |\n",
    "| 2001 |  6 |  43 |  83 |  57 |  90 |  76 | 135 | 201 | 173 | 141 | 142 |  95 |  46 |\n",
    "| 2001 |  7 |  49 |  85 |  86 |  69 |  90 | 110 | 185 | 161 | 153 | 128 | 112 |  62 |\n",
    "| 2001 |  8 |  51 |  34 |  87 |  71 |  93 | 102 | 163 | 155 | 147 | 127 |  50 |  40 |\n",
    "| 2001 |  9 |  22 |  21 |  90 | 101 | 102 | 101 | 157 | 141 | 121 | 132 |  33 |  16 |\n",
    "| 2001 | 10 |  23 |  51 | 102 |  96 | 147 | 110 | 160 | 152 | 119 | 125 |  44 |  19 |\n",
    "| 2001 | 11 |  29 |  81 | 101 |  72 | 172 | 106 | 141 | 152 | 142 | 150 |  78 |  20 |\n",
    "| 2001 | 12 |  40 |  79 |  60 |  74 | 168 | 138 | 144 | 171 | 142 | 153 | 101 |  46 |\n",
    "| 2001 | 13 |  31 |  40 |  57 |  57 | 165 | 148 | 143 | 195 | 124 | 160 |  53 |  47 |\n",
    "| 2001 | 14 |  24 |  35 |  64 |  75 | 118 | 144 | 132 | 205 | 124 | 155 |  40 |  15 |\n",
    "| 2001 | 15 |  18 |  44 |  53 |  91 | 120 | 153 | 127 | 210 | 129 | 154 |  43 |  13 |\n",
    "| 2001 | 16 | -12 |  46 |  54 |  73 | 128 | 138 | 133 | 176 | 125 | 117 |  55 |  23 |\n",
    "| 2001 | 17 |  -9 |  21 |  26 |  52 |  89 | 123 | 143 | 157 | 109 | 138 |  83 |  20 |\n",
    "| 2001 | 18 |  -5 |   3 |  28 |  44 | 101 | 128 | 149 | 167 | 124 | 147 |  73 |  38 |\n",
    "| 2001 | 19 |  -8 |  26 |  20 |  54 | 119 | 160 | 137 | 181 | 129 | 113 |  63 |  48 |\n",
    "| 2001 | 20 |  -5 |  36 |  22 |  54 | 130 | 156 | 146 | 169 | 138 | 135 |  67 |  26 |\n",
    "| 2001 | 21 |  13 |  62 |  26 |  52 | 129 | 140 | 153 | 159 | 116 | 104 |  96 |  41 |\n",
    "| 2001 | 22 |  45 |  73 |  60 |  64 | 131 | 139 | 169 | 179 | 119 | 118 |  91 |  12 |\n",
    "| 2001 | 23 |  69 |  49 |  77 |  84 | 150 | 160 | 170 | 179 | 103 | 137 |  48 |  18 |\n",
    "| 2001 | 24 |  69 |  27 |  68 |  68 | 157 | 173 | 155 | 191 | 127 | 130 |  68 |  34 |\n",
    "| 2001 | 25 |  48 |  14 |  49 |  85 | 137 | 191 | 181 | 193 | 123 | 124 | 115 |  45 |\n",
    "| 2001 | 26 |  47 |  27 |  38 |  90 | 157 | 219 | 184 | 171 | 112 | 124 |  51 |  27 |\n",
    "| 2001 | 27 |  40 |  27 |  50 |  84 | 160 | 187 | 194 | 152 | 145 | 122 |  48 |  40 |\n",
    "| 2001 | 28 |  11 |  23 |  70 |  99 | 176 | 170 | 206 | 143 | 166 | 116 |  76 |  43 |\n",
    "| 2001 | 29 |  20 |-999 |  66 |  76 | 160 | 177 | 230 | 149 | 168 | 106 |  96 |  18 |\n",
    "| 2001 | 30 |  25 |-999 |  83 |  80 | 149 | 170 | 201 | 154 | 141 | 140 | 124 |  15 |\n",
    "| 2001 | 31 |  40 |-999 | 100 |-999 | 130 |-999 | 192 | 146 |-999 | 101 |-999 |  -4 |\n",
    "| 2002 |  1 | -18 |  86 |  45 | 113 |  93 | 143 | 131 | 161 | 126 | 145 | 114 |  87 |\n",
    "| 2002 |  2 | -18 | 109 |  31 | 100 |  84 | 162 | 135 | 156 | 140 | 156 | 114 |  83 |\n",
    "+------+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prerequesites\n",
    "from pyhive import hive\n",
    "%load_ext sql\n",
    "%sql hive://cloudera@quickstart.cloudera:10000/sqlzoo\n",
    "%config SqlMagic.displaylimit = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Days, Months and Years\n",
    "The units are 10th of a degree Celcius. The columns are yr and dy for year and day of month. The next twelve columns are for January through to December.\n",
    "\n",
    "**Show the average daily temperature for August 10th 1964**\n",
    "\n",
    "```sql\n",
    "show create table hadcet\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * hive://cloudera@quickstart.cloudera:10000/sqlzoo\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>m8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>139</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(139,)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT m8 FROM hadcet\n",
    "  WHERE yr=1964 AND dy=10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. preteen Dickens\n",
    "\n",
    "Charles Dickens is said to be responsible for the tradition of expecting snow at Christmas [Daily Telegraph](http://www.telegraph.co.uk/topics/christmas/3933091/Dreaming-of-a-white-Christmas-Blame-the-nostalgia-of-Charles-Dickens-snowy-childhood.html). Show the temperature on Christmas day (25th December) for each year of his childhood. He was born in February 1812 - so he was 1 (more or less) in December 1812.\n",
    "\n",
    "**Show the twelve temperatures.**\n",
    "\n",
    "```sql\n",
    "SELECT yr-1811 as age FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 and 1812+12 AND dy=25\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * hive://cloudera@quickstart.cloudera:10000/sqlzoo\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>age</th>\n",
       "        <th>m12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>4</td>\n",
       "        <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>5</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>6</td>\n",
       "        <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7</td>\n",
       "        <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>8</td>\n",
       "        <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>9</td>\n",
       "        <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>10</td>\n",
       "        <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>11</td>\n",
       "        <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>12</td>\n",
       "        <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>13</td>\n",
       "        <td>104</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 0),\n",
       " (2, 81),\n",
       " (3, 11),\n",
       " (4, -8),\n",
       " (5, 30),\n",
       " (6, -4),\n",
       " (7, 10),\n",
       " (8, -8),\n",
       " (9, -3),\n",
       " (10, 35),\n",
       " (11, 5),\n",
       " (12, 78),\n",
       " (13, 104)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT yr-1811 AS age, m12 FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 AND 1812+12 AND dy=25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Minimum Temperature Before Christmas\n",
    "\n",
    "We declare a White Christmas if there was a day with an average temperature below zero between 21st and 25th of December.\n",
    "\n",
    "**For each age 1-12 show which years were a White Christmas. Show 'White Christmas' or 'No snow' for each age.**\n",
    "\n",
    "```sql\n",
    "SELECT yr-1811 as age FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 and 1812+12 AND dy=25\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * hive://cloudera@quickstart.cloudera:10000/sqlzoo\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>age</th>\n",
       "        <th>lbl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>4</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>5</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>6</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>8</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>9</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>10</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>11</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>12</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 'No snow'),\n",
       " (2, 'No snow'),\n",
       " (3, 'White Christmas'),\n",
       " (4, 'White Christmas'),\n",
       " (5, 'White Christmas'),\n",
       " (6, 'White Christmas'),\n",
       " (7, 'White Christmas'),\n",
       " (8, 'White Christmas'),\n",
       " (9, 'White Christmas'),\n",
       " (10, 'No snow'),\n",
       " (11, 'White Christmas'),\n",
       " (12, 'No snow')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT yr-1811 AS age, CASE WHEN MIN(m12)<0 \n",
    "    THEN 'White Christmas' ELSE 'No snow' END lbl\n",
    "      FROM hadcet\n",
    "    WHERE yr BETWEEN 1812 AND 1812+11 AND dy BETWEEN 21 AND 25\n",
    "    AND m12 != -999\n",
    "    GROUP BY yr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. White Christmas Count\n",
    "\n",
    "A person's White Christmas Count (wcc) is the number of White Christmases they were exposed to as a child (between 3 and 12 inclusive assuming they were born at the beginning of the year and were about 1 year old on their first Christmas).\n",
    "\n",
    "Charles Dickens's wcc was 8.\n",
    "\n",
    "**List all the years and the wcc for children born in each year of the data set. Only show years where the wcc was at least 7.**\n",
    "\n",
    "```sql\n",
    "SELECT yr-1811 as age FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 and 1812+12 AND dy=25\n",
    "```\n",
    "\n",
    "```\n",
    "+------+-----+\n",
    "| yob  | wcc |\n",
    "+------+-----+\n",
    "| 1805 |   7 |\n",
    "| 1806 |   7 |\n",
    "| 1807 |   7 |\n",
    "| 1808 |   8 |\n",
    "| 1809 |   9 |\n",
    "| 1810 |   8 |\n",
    "| 1811 |   8 |\n",
    "| 1812 |   8 |\n",
    "| 1813 |   7 |\n",
    "+------+-----+\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * hive://cloudera@quickstart.cloudera:10000/sqlzoo\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>yr</th>\n",
       "        <th>wcc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1809</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1810</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1811</td>\n",
       "        <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1812</td>\n",
       "        <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1813</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1809, 7), (1810, 7), (1811, 8), (1812, 8), (1813, 7)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT x.yr, COUNT(y.yr) wcc\n",
    "FROM (SELECT DISTINCT yr FROM hadcet) x JOIN (\n",
    "   SELECT yr FROM hadcet \n",
    "    WHERE dy BETWEEN 21 AND 25 AND m12 != -999\n",
    "    GROUP BY yr \n",
    "    HAVING SUM(CASE WHEN m12 < 0 THEN 1 ELSE 0 END) > 0\n",
    ") y ON (1=1)\n",
    "WHERE y.yr >= x.yr + 2 AND y.yr <= x.yr + 11\n",
    "GROUP BY x.yr\n",
    "HAVING COUNT(y.yr) >= 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Climate Change\n",
    "\n",
    "Here are the average temperatures for August by decade. You decide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * hive://cloudera@quickstart.cloudera:10000/sqlzoo\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>decade</th>\n",
       "        <th>avg_m8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1770.0</td>\n",
       "        <td>164.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1780.0</td>\n",
       "        <td>159.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1790.0</td>\n",
       "        <td>155.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1800.0</td>\n",
       "        <td>161.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1810.0</td>\n",
       "        <td>153.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1820.0</td>\n",
       "        <td>151.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1830.0</td>\n",
       "        <td>154.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1840.0</td>\n",
       "        <td>153.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1850.0</td>\n",
       "        <td>150.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1860.0</td>\n",
       "        <td>155.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1870.0</td>\n",
       "        <td>157.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1880.0</td>\n",
       "        <td>156.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1890.0</td>\n",
       "        <td>148.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1900.0</td>\n",
       "        <td>154.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1910.0</td>\n",
       "        <td>153.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1920.0</td>\n",
       "        <td>150.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1930.0</td>\n",
       "        <td>158.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1940.0</td>\n",
       "        <td>162.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1950.0</td>\n",
       "        <td>158.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1960.0</td>\n",
       "        <td>154.69</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<span style=\"font-style:italic;text-align:center;\">26 rows, truncated to displaylimit of 20</span>"
      ],
      "text/plain": [
       "[(1770.0, 164.53),\n",
       " (1780.0, 159.86),\n",
       " (1790.0, 155.47),\n",
       " (1800.0, 161.7),\n",
       " (1810.0, 153.61),\n",
       " (1820.0, 151.37),\n",
       " (1830.0, 154.82),\n",
       " (1840.0, 153.34),\n",
       " (1850.0, 150.29),\n",
       " (1860.0, 155.83),\n",
       " (1870.0, 157.08),\n",
       " (1880.0, 156.19),\n",
       " (1890.0, 148.9),\n",
       " (1900.0, 154.99),\n",
       " (1910.0, 153.31),\n",
       " (1920.0, 150.68),\n",
       " (1930.0, 158.21),\n",
       " (1940.0, 162.31),\n",
       " (1950.0, 158.09),\n",
       " (1960.0, 154.69),\n",
       " (1970.0, 155.48),\n",
       " (1980.0, 164.26),\n",
       " (1990.0, 156.69),\n",
       " (2000.0, 173.0),\n",
       " (2010.0, 159.64),\n",
       " (2020.0, 164.61)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = %sql SELECT decade, ROUND(AVG(m8), 2) avg_m8 FROM \\\n",
    "(SELECT ROUND(yr/10,0)*10 decade, \\\n",
    "  CASE WHEN m8=-999 THEN NULL ELSE m8 END m8 \\\n",
    "  FROM hadcet) a \\\n",
    "GROUP BY decade\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.DataFrame(df)\n",
    "df.columns=['decade', 'avg_m8']\n",
    "df['avg_m8'] = df['avg_m8']/10\n",
    "plt.plot(df['decade'], df['avg_m8'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
